Observation Engine for Battery-Efficient Circumstance Detection
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Solution Overview
Problem
Existing mobile device applications lack the ability to efficiently detect and respond to user-specific circumstances, such as location, activity, and external events, without relying on continuous network connections, which can drain battery power and fail to leverage the wealth of available sensor data effectively.
Innovation Solution
An observation engine, integrated as an SDK, monitors sensor data and operating system properties to detect predefined circumstances, allowing applications to respond intelligently and efficiently, with over-the-air updates for configuration and rule adjustments, and supports battery-efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous network connection is used to detect user circumstances, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The system uses locally-available sensor data and device information to autonomously detect circumstances without requiring continuous external network verification. The observation engine self-sufficiently processes accelerometer data, location information, and device state to identify user circumstances, eliminating the need for constant network connectivity while maintaining detection accuracy.
Solution Approach 2:
The patent introduces an observation engine as an intermediary layer between raw sensor data and application logic. This intermediary processes and interprets sensor inputs locally, transforming raw data into meaningful circumstance detections without requiring continuous network intervention, thus reducing power consumption while preserving detection capabilities.
2Reliability
If sensor data is continuously monitored to detect circumstances, then detection reliability is improved, but battery power is drained
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic sampling of sensor data at optimized intervals. The observation engine selectively activates sensors and processes data only when necessary based on current device state and predefined triggers, maintaining reliable circumstance detection while significantly reducing energy consumption compared to continuous monitoring approaches.
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as sampling frequency and sensor activation thresholds based on current operational conditions. When the device is in low-activity states, monitoring intensity is reduced; when activity increases or specific circumstances are suspected, monitoring intensity increases, optimizing the balance between detection reliability and energy consumption.
3Adaptability or versatility
If applications process raw sensor data directly, then detection flexibility is improved, but device complexity increases
Solution Approach 1:
The observation engine serves as an intermediary that abstracts complex sensor processing logic from applications. It provides a standardized interface for applications to query circumstance detections without requiring them to implement complex sensor fusion algorithms, thereby maintaining detection flexibility while reducing application complexity.
Solution Approach 2:
The system segments the detection architecture into distinct layers: raw sensor data collection, processing and interpretation by the observation engine, and application-level utilization of detected circumstances. This segmentation allows each layer to specialize in specific functions, improving overall flexibility while distributing complexity across multiple components rather than concentrating it in applications.
Data Source
AI summary
Methods and systems are provided that may be utilized to detect occurrence of one or more specified circumstances. A determination may be made as to whether one or more specified circumstances are detected such as responsive to one or more user actions or an occurrence of an event unrelated to a user. One or more binary digital signals may be generated to store a detection of one or more specified user circumstances in a log or memory at least partially in response to detection of the one or more specified circumstances.