Unified Activity Data Stream from Multiple Devices
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Solution Overview
Problem
Users face challenges in reconciling conflicting data from multiple biometric and environmental monitoring devices, which can provide inaccurate or overlapping information about physical activities, making it difficult to determine the most accurate data for tracking user activity.
Innovation Solution
A system and method for consolidating overlapping data streams from multiple devices by using a cloud-based server or local processor to combine and prioritize data streams based on accuracy, precision, and user preferences, creating a unified activity data stream that organizes data segments chronologically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple monitoring devices is collected to improve tracking coverage, then the quantity of activity data increases, but data accuracy and reliability deteriorate due to conflicting information from different devices
Solution Approach 1:
The patent segments the data processing task by dividing data streams into time-based segments and device-specific segments. Each device's data is processed independently through its own algorithm, and segments are then merged chronologically. This allows the system to handle multiple data sources while maintaining the ability to evaluate and select the most accurate data for each time segment, thus resolving the contradiction between data quantity and reliability.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between multiple monitoring devices and the final output. This intermediary system applies consolidation algorithms to evaluate, compare, and reconcile data from different devices, selecting the most reliable data segments while filtering out conflicting or inaccurate information. This mediator enables the system to utilize data from multiple devices without directly propagating their conflicts to the final result.
2Measurement precision
If multiple devices are used to monitor user activity, then measurement coverage improves, but device complexity increases due to the need to reconcile conflicting data streams
Solution Approach 1:
The system segments the complex reconciliation task into manageable components: individual device data processing, time-based segmentation, and hierarchical merging. Each device's data stream is processed through its own consolidation algorithm independently, then segments are merged in chronological order with conflict resolution at each level. This segmentation reduces overall complexity by breaking down the monolithic reconciliation problem into smaller, manageable sub-tasks.
Solution Approach 2:
The patent implements dynamic data consolidation where the system adaptively selects and weights data from different devices based on real-time conditions, device reliability metrics, and data quality indicators. The consolidation algorithms dynamically adjust which device's data is prioritized for each time segment, allowing the system to handle varying levels of device performance and conflict scenarios without requiring a fixed, overly complex reconciliation structure.
3Loss of information
If data from multiple sources is consolidated to provide comprehensive activity tracking, then data completeness improves, but processing time increases due to the need to organize and merge multiple data streams
Solution Approach 1:
The patent applies preliminary action by pre-segmenting data streams into time-based chunks and pre-processing each device's data through its own consolidation algorithm before merging. This preliminary organization of data into manageable, chronologically-ordered segments significantly reduces the computational complexity of the final merging process. By preparing data in advance with proper segmentation and initial consolidation, the system minimizes the time required for the actual data merging and reconciliation operations.
Solution Approach 2:
The system dynamically adjusts processing priorities and merges data streams in real-time based on device data availability and quality. Rather than processing all data streams uniformly, the system dynamically selects which device segments to process and merge at each time step, optimizing processing time while maintaining data completeness. This dynamic approach allows the system to handle variable data loads efficiently without requiring exhaustive processing of all possible data combinations.
Data Source
AI summary
Methods, devices, and computer programs are presented for creating a unified data stream from multiple data streams acquired from multiple devices. One method includes an operation for receiving activity data streams from the devices, each activity data stream being associated with physical activity data of a user. Further, the method includes an operation for assembling the unified activity data stream for a period of time. The unified activity data stream includes data segments from the data streams of at least two devices, and the data segments are organized time-wise over the period of time.