Assistant Auto-Capture Using Cascading Sensor Triggers
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Solution Overview
Problem
Existing systems face challenges in detecting and capturing interesting moments during user activities without user intervention, while efficiently managing computing resources and ensuring privacy.
Innovation Solution
The assistant system utilizes a hybrid architecture combining client-side and server-side processes, employing various sensors and machine-learning models to detect context changes and determine interesting moments, using cascading modeling policies to optimize sensor usage and conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system continuously monitors all sensor data and uses expensive machine-learning models to detect interesting moments, then the accuracy of moment detection is improved, but the computing resources and energy consumption increase significantly
Solution Approach 1:
The patent segments the detection process into two stages: first using cheap sensors (accelerometer, gyroscope, microphone) to detect potential interesting moments by monitoring context changes, and then selectively applying expensive machine-learning models only when needed. This segmentation allows the system to maintain high detection accuracy while minimizing computing resource consumption by avoiding continuous use of resource-intensive models.
Solution Approach 2:
The system applies different levels of processing quality to different data streams based on their importance and resource requirements. Cheap sensors provide continuous monitoring with lower processing quality, while expensive machine-learning models provide high-quality analysis only for selected moments. This local quality differentiation optimizes the balance between detection accuracy and resource usage.
2Measurement precision
If the system uses expensive machine-learning models for continuous analysis, then the detection accuracy is improved, but the latency increases due to heavy computation
Solution Approach 1:
The system performs preliminary detection using cheap, fast sensors to identify potential interesting moments before applying expensive machine-learning models. This preliminary action filters out most non-interesting moments, allowing the system to maintain low latency while still achieving high detection accuracy through selective use of advanced models.
Solution Approach 2:
The system skips the expensive machine-learning model analysis for most time points, rushing through detection using only cheap sensors. Only when the cheap sensors indicate a potential interesting moment does the system pause to apply the more accurate but slower machine-learning models, thereby minimizing overall latency.
3Quantity of substance
If the system captures all moments during user activities, then the completeness of captured moments is improved, but the data storage and processing requirements increase
Solution Approach 1:
The system dynamically adjusts the capture threshold based on context changes detected by cheap sensors. When significant context changes are detected, the system increases capture frequency to ensure completeness. When no significant changes occur, the system reduces or stops capture, thereby managing data volume dynamically while maintaining completeness of interesting moments.
Solution Approach 2:
The system uses the cheap sensor data to automatically identify and flag potential interesting moments, serving itself in the selection process. This self-service mechanism eliminates the need for manual configuration of capture parameters and automatically optimizes the balance between capturing completeness and data management complexity.
Data Source
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AI summary
In one embodiment, a method includes accessing from a client system associated with a first user sensor signals captured by sensors of the client system, wherein the client system comprises a plurality of sensors, and wherein the sensors signals are accessed from the sensors based on cascading model policies, wherein each cascading model policy utilizes one or more of a respective cost or relevance associated with each sensor, detecting a change in a context of the first user associated with an activity of the first user based on machine-learning models and the sensor signals, wherein the change in the context of the first user satisfies a trigger condition associated with the activity, and responsive to the detected change in the context of the first user automatically capturing visual data by cameras of the client system.