Passive Interaction Classification via Device Context Awareness
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Solution Overview
Problem
Current human-device interaction systems face challenges with classification latency, reliance on cloud-based processing, limited accuracy, power-hungry hardware, and inability to measure engagement or context on mobile devices, particularly struggling with fraudulent traffic and user privacy issues, while lacking efficient methods to identify passive user behavior.
Innovation Solution
A system utilizing device-dependent training and multi-modal signal analysis with sensors to generate context-aware signatures for passive human-device interactions, reducing classification latency and leveraging low-power sensors to track subtle user behaviors without active user input, thereby improving precision and reducing resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based processing is used for classification, then processing power is improved, but classification latency and network dependency increase
Solution Approach 1:
The patent segments the classification system into two parts: a cloud-based training phase that generates classification models, and a device-based inference phase that executes these models locally. This segmentation allows heavy processing to occur in the cloud during offline training, while real-time classification happens locally with minimal latency.
Solution Approach 2:
The system performs preliminary action by pre-training classification models in the cloud before deployment to mobile devices. These pre-trained models are then executed locally on the device, eliminating the need for real-time cloud processing and reducing classification latency significantly.
2Measurement precision
If power-hungry hardware like tactile screens and cameras are used, then interaction accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent extracts and removes the need for power-hungry hardware components like tactile screens and cameras by utilizing only the device's existing sensors (accelerometer, gyroscope, magnetometer, proximity sensor, light sensor). This extraction eliminates unnecessary energy consumption while maintaining classification functionality.
Solution Approach 2:
The system leverages sensors that are already present and running on mobile devices for other purposes, making them serve dual functions. These sensors continuously collect data that can be used for interaction classification without requiring additional power-hungry hardware, thus the device serves itself for classification purposes.
3Ease of operation
If additional user interfaces and menus are added, then user control is improved, but ease of operation deteriorates due to navigation time and learning curve
Solution Approach 1:
The system operates passively in the background, automatically classifying user interactions without requiring active user control through menus or interfaces. The classification happens autonomously based on sensor data, eliminating navigation time and learning curves associated with traditional UI controls.
Solution Approach 2:
The patent accelerates the interaction classification process by using real-time sensor data and pre-trained models to immediately classify user actions as they occur, rather than requiring users to navigate through menus or provide explicit input. This accelerated process eliminates delays and improves ease of operation.
4Device complexity
If screen-only data and past behaviors are used for classification, then simplicity is maintained, but measurement precision of engagement and context deteriorates
Solution Approach 1:
The patent transitions from static screen-only data to dynamic multi-modal sensor data that captures real-time physical context. By continuously monitoring accelerometer, gyroscope, magnetometer, proximity sensor, and light sensor data, the system dynamically adapts to user behavior and environmental context, significantly improving engagement measurement accuracy while maintaining reasonable system complexity.
Data Source
AI summary
A system and method are provided that use context awareness with device-dependent training to improve precision while reducing classification latency and the need for additional computing, such as by relying on cloud-based processing. Moreover, the following can leverage signal analysis with multiple sensors and secondary validation in a multi-modal approach to track passive events that would otherwise be difficult to identify using classical methods. In at least one implementation, the system and method described herein can leverage low power sensors and integrate already available human behavior in modular algorithms isolating specific context to reduce user interact time and training to a minimum.