Ubiquitous Network Encounter Correlation for Pandemic Tracking
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Solution Overview
Problem
Current methods for tracking and tracing social interactions during pandemics or incidents are manual and inefficient, failing to effectively stem the spread of diseases or identify relevant encounters.
Innovation Solution
A system and method utilizing ubiquitous networks to periodically receive data from devices associated with entities, determining temporal locations, correlating encounters within predefined proximity to incidents, and adding registered encounters to incidence data, with options for notifications based on predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used for tracking and tracing social interactions, then implementation simplicity is maintained, but tracking efficiency and disease spread control are insufficient
Solution Approach 1:
The patent applies universality by creating a multi-functional system that simultaneously performs location tracking, encounter detection, disease spread modeling, and contact tracing. The ubiquitous network infrastructure serves multiple purposes: it tracks temporal locations of entities, identifies encounters within proximity thresholds, and feeds data to disease spread models, all through a single integrated system rather than separate manual processes
Solution Approach 2:
The patent uses an intermediary approach by introducing a central server that acts as a mediator between devices and the disease spread model. The server receives temporal location data from multiple devices, processes encounter correlations, and generates insights for disease spread prediction. This intermediary infrastructure enables automated tracking without requiring direct complex interactions between all entities
2Speed
If ubiquitous networks are deployed for automatic tracking, then tracking coverage and speed are improved, but data privacy and security concerns increase
Solution Approach 1:
The patent applies local quality by implementing proximity-based encounter detection that only processes data when entities are within a defined threshold distance. The system doesn't continuously track or store all location data, but rather processes information locally when specific conditions (proximity encounters) are met, reducing overall data collection and privacy exposure while maintaining effective contact tracing
3Measurement precision
If continuous monitoring of all entities is performed, then encounter detection accuracy is improved, but energy consumption and computational load increase
Solution Approach 1:
The patent implements periodic action by having devices report temporal locations at scheduled intervals rather than continuously. The system periodically receives data from devices, processes encounters based on these periodic updates, and maintains adequate encounter detection accuracy through this rhythm-based approach, significantly reducing energy consumption compared to continuous monitoring
Solution Approach 2:
The patent applies partial action by focusing computational resources only on processing data when encounters are detected or when entities are within relevant proximity thresholds. Rather than analyzing all location data equally, the system selectively processes partial sets of data that are most relevant to disease spread modeling, reducing overall computational load while maintaining detection accuracy
Data Source
AI summary
A method includes: periodically receiving data via a ubiquitous network from each of a plurality of devices, where each of the devices is associated with one of the plurality of entities and has a unique identifier; determining, as a function of the received data, a plurality of temporal locations for each of the plurality of entities, each of the temporal locations identifying a time and place where an associated entity was located; and, correlating encounters between two or more of the plurality of entities with incidence data associated with an incident, wherein an encounter is registered when a temporal location of a first of the plurality of entities is within a predefined proximity of at least a second of the plurality of entities within a physical zone associated with the incident.