Proximal Grouping of Wireless Signals via Temporal Persistence
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Solution Overview
Problem
Current systems for determining proximal relationships between electronic devices are inefficient and unable to produce real-time results, as they rely on location information that may not be available due to privacy and security concerns, and require expensive calculations to approximate missing location data.
Innovation Solution
The method generates proximal groupings of wireless signals based on temporal persistence and spatial proximity observed by multiple observer devices, without requiring the knowledge of the devices' locations, using a server that receives and processes identification information from observer devices to create proximal groupings of signals that are temporally persistent and spatially proximate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional clustering algorithms are used to determine proximal relationships, then location information can be processed, but the system cannot operate in real-time and requires expensive calculations to approximate missing location data
Solution Approach 1:
The patent replaces traditional location-based clustering algorithms with a signal observation-based system. Instead of using mechanical location calculations and approximations, the system uses wireless signal observations from multiple observer devices to determine proximal groupings, eliminating the need for expensive location data approximation calculations and enabling real-time operation
Solution Approach 2:
The patent introduces wireless signals as an intermediary medium to establish proximal relationships between electronic devices. Rather than directly calculating locations or using location data, the system uses signal observations as a mediator to infer spatial relationships, which resolves the contradiction by providing real-time grouping capability without expensive location calculations
2Measurement precision
If location information is collected from electronic devices, then proximal relationships can be determined, but privacy and security concerns prevent devices from broadcasting or masking their locations
Solution Approach 1:
The patent uses wireless signals as an intermediary to indirectly determine proximal relationships without requiring direct location information from devices. This resolves the contradiction by maintaining measurement precision through signal-based inference while preserving privacy and security by not requiring devices to broadcast or share their location data
Solution Approach 2:
The patent substitutes the location information collection mechanism with a signal observation mechanism. Instead of relying on devices to provide location data (which creates privacy concerns), the system observes wireless signals emitted by devices, thereby achieving accurate proximal relationship determination without compromising device privacy or security
3Adaptability or versatility
If approximations for missing location data are calculated from other signals' locations, then proximal groupings can be formed, but the calculation is expensive and inefficient
Solution Approach 1:
The patent replaces the complex calculation system for approximating location data with a direct signal observation system. Instead of performing expensive calculations to infer locations from other signals, the system directly uses signal observations from multiple observer devices to determine proximal groupings, thereby reducing computational complexity while maintaining adaptability
Solution Approach 2:
The patent extracts the essential information needed for proximal grouping directly from wireless signal observations, eliminating the need for complex location approximation calculations. By taking out only the necessary signal observation data and using it directly for grouping, the system reduces computational complexity while preserving grouping capability
Data Source
AI summary
Embodiments described herein generate proximal groupings of wireless signals based upon the temporal persistence and spatial proximity of the wireless signals as observed by a plurality of observer devices. For example, a first observer device may observe a first set of wireless signals at a first timepoint and a second observer device may observe a second set of wireless signals at a second timepoint. The first observer device may again observe a third set of wireless signals at a third timepoint. Based upon these observations, a server may generate a proximal grouping a wireless signals containing a subset of the first, second, third of wireless signals based upon temporal persistence and spatial proximity. Temporal persistence may be based upon the repeated observations of the subset of wireless signals across different timepoints and the spatial proximity may be based upon the proximity of locations of the observer devices.