Multi-Device Context Store for Unified User Scheduling
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Solution Overview
Problem
Modern computing devices often collect incomplete user behavior and computing needs data from a single device, failing to provide a comprehensive picture of user context, which limits their ability to predict and schedule tasks optimally.
Innovation Solution
A multi-device context store system that collects and shares context information across multiple devices, allowing user devices to determine a clearer user context and schedule tasks based on both current and predicted optimal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single device collects user context information, then the device complexity is reduced, but the completeness and accuracy of user context data deteriorates
Solution Approach 1:
The patent merges context information from multiple devices into a unified context store. The system combines data from various user devices (mobile phones, tablets, computers, wearables) to create a comprehensive view of user context, thereby resolving the contradiction between system simplicity and information completeness by consolidating distributed data sources into a centralized structure.
Solution Approach 2:
The context store is designed as a universal system that can collect, store, and process context information from multiple different device types and sources. This multi-functional approach allows the same system architecture to handle diverse data sources (location, activity, device state) across different devices without requiring device-specific implementations.
2Speed
If tasks are scheduled based on current context only, then the response time is reduced, but the optimality of task execution deteriorates
Solution Approach 1:
The system performs preliminary analysis of historical context data to predict future user context states. By analyzing patterns in historical data (user behavior, device usage patterns, contextual conditions), the system can anticipate optimal times for task execution and schedule tasks in advance, thereby achieving both fast response and high optimality.
Solution Approach 2:
The system uses historical context data as feedback to continuously improve task scheduling decisions. By comparing predicted context with actual context outcomes, the system refines its predictions and scheduling algorithms, enabling it to balance immediate response requirements with long-term execution optimality through iterative learning.
Data Source
AI summary
In some implementations, a user device can maintain a multi-device context store. For example, the user device can receive device and/or user context information from multiple devices and store the context information in a local data store. The user device can collect local device and/or user context information and store the context information in the local context store. The user device can receive user/device context queries from client processes and send the client processes user/device context information from multiple devices in response to the queries.