Activity-Based Device Recommendations Using User State Detection
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Solution Overview
Problem
Existing systems lack an efficient method to determine the activity state of users within environments, such as homes or vehicles, which is crucial for optimizing device operations and user interactions, as they rely on manual inputs or limited sensor data.
Innovation Solution
A system utilizing machine learning models, including supervised models and neural networks, that analyze data from various devices and sensors to classify user activity states as active, asleep, or away, by integrating data from devices like smart home devices, motion sensors, and audio detection, and predicting future states based on historical and real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inputs or limited sensor data are used to determine user activity state, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system segments the activity state determination into multiple independent analysis components: motion sensor data analysis, audio sensor data analysis, device usage pattern analysis, and machine learning model processing. Each component processes specific types of data independently, then combines results to achieve high measurement precision without requiring a single complex system
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process and interpret data from multiple sensors and devices. These models act as mediators between raw sensor data and activity state determination, automatically extracting meaningful patterns and reducing the need for complex rule-based systems while improving accuracy
2Measurement precision
If multiple devices and sensors are integrated to analyze user activity, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system employs universal data processing architectures and machine learning models that can handle multiple types of sensor data (motion, audio, device usage) through a common framework. This multi-functional approach allows the same core processing infrastructure to analyze diverse data sources, reducing overall system complexity while maintaining high measurement precision
Solution Approach 2:
The patent merges data from multiple independent sources (motion sensors, audio sensors, device usage logs) into a unified activity state determination process. By combining these data streams through machine learning models, the system achieves improved measurement precision while managing complexity through integrated processing rather than separate analysis systems
3Productivity
If automated machine learning models are used to determine activity state, then productivity improves, but device complexity increases
Solution Approach 1:
The system performs preliminary data processing and feature extraction before feeding data to machine learning models. Motion sensor data, audio sensor data, and device usage patterns are pre-processed and organized into standardized formats, reducing the computational complexity required during real-time activity state determination while maintaining high productivity
Solution Approach 2:
The machine learning models operate autonomously to determine activity states without requiring manual configuration or intervention. The system self-adjusts by continuously processing incoming sensor data and device usage information, automatically updating activity state classifications based on learned patterns, thereby achieving high productivity with managed complexity through autonomous operation
Data Source
AI summary
Systems and methods for activity-based device recommendations are disclosed. For example, historical usage data associated with a device may indicate that the device is likely to be associated with a given state at a given time. When the device is not in the anticipated state, a recommendation to transition the device state, for example, may be sent. Additionally, a determination of the activity state associated with the device, such as an active state, an asleep state, and/or an away state may be utilized to determine the recommendation to surface, to determine whether to send a recommendation, and when and/or how to send the recommendation.