Gaze-Guided 3D Scene Assistance with Adaptive Event Sampling
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Solution Overview
Problem
Existing computer systems struggle to provide timely, accurate, and relevant assistance to users interacting with three-dimensional scenes, leading to inefficient user interactions and increased power consumption.
Innovation Solution
A computer system that detects user gaze and scene data using sensor devices, determines semantic information, and performs actions based on event criteria to assist the user, utilizing a computer-executable plan adjusted by sensor parameter changes when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the computer system continuously monitors scene data and detects events to provide timely assistance, then the responsiveness and helpfulness of the system improve, but the power consumption increases
Solution Approach 1:
The system employs periodic sampling of scene data at adjustable intervals rather than continuous monitoring. The processor can change the sampling rate based on scene complexity, user presence detection, and event likelihood, thereby reducing power consumption while maintaining adequate responsiveness for meaningful events.
Solution Approach 2:
The system performs preliminary actions by pre-processing scene data to identify potential events before full analysis is required. By detecting preliminary indicators of events (such as sudden movements or object interactions) and preparing appropriate responses in advance, the system can respond quickly without needing continuous high-power monitoring.
2Measurement precision
If the system processes detailed semantic information about scenes to improve accuracy of event detection, then the precision of assistance improves, but the processing time and computational load increase
Solution Approach 1:
The system segments the scene analysis process into multiple stages: initial low-resolution preprocessing to identify potential events, followed by selective high-resolution semantic analysis only when necessary. This hierarchical approach maintains accuracy for complex events while reducing overall processing time by avoiding exhaustive analysis of all scene data continuously.
Solution Approach 2:
The system applies different levels of processing quality to different parts of the scene based on their importance. Areas or objects that are more likely to contain meaningful events receive detailed semantic analysis, while less significant regions are processed at lower detail levels, thereby reducing total computational load while maintaining accuracy where it matters most.
3Adaptability or versatility
If the system provides comprehensive assistance actions based on detected events, then the helpfulness and completeness of assistance improve, but the complexity of system response increases
Solution Approach 1:
The system dynamically adjusts the scope and complexity of assistance actions based on the detected event type, user context, and situational factors. For simple events, the system provides minimal appropriate responses, while complex events trigger more comprehensive assistance sequences. This dynamic adaptation maintains versatility without requiring the system to always operate at maximum complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where user responses and contextual changes modify the assistance actions. By continuously monitoring user interaction and adjusting subsequent actions based on feedback, the system can provide comprehensive assistance adaptively rather than through rigid complex predetermined sequences, reducing inherent system complexity while maintaining versatility.
Data Source
AI summary
An example process includes: while a computer system is present within a first scene, detecting a first gaze of a user; after a determination of semantic information about the first scene based on the detected first gaze of the user and while the computer system is present within a second scene, detecting data corresponding to the second scene; and in response to detecting the data corresponding to the second scene: in accordance with a determination that an event that occurs in the second scene is detected based on the data corresponding to the second scene and that the event satisfies a set of one or more event criteria, performing a set of one or more actions that correspond to assisting the user with the event, where a first action of the set of one or more actions is based on the semantic information about the first scene.