Velocity-Weighted UE Location Analysis for Event Attribution
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Solution Overview
Problem
Conventional systems for analyzing user equipment (UE) location data fail to accurately attribute changes in UE movement to specific events, especially when multiple events occur in the vicinity of a point of interest (POI), leading to incorrect attribution of traffic patterns and neglecting the impact of surrounding events.
Innovation Solution
A velocity-weighted analysis of UE location data that considers both velocity and dwell time to determine interaction probabilities with POIs, accounting for historical movement patterns and location accuracy to adapt geographical areas associated with UE routes, allowing for attribution of effects to combinations of events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional UE location data analysis is used to determine path of travel and correlate to POI, then basic movement tracking is achieved, but accurate attribution of UE behavior changes to specific events fails when multiple events occur in vicinity
Solution Approach 1:
The patent segments the analysis by introducing velocity-weighted dwelling values that differentiate between various POIs along the UE path. Instead of treating all POIs uniformly, the system calculates separate dwelling values for each POI based on UE velocity and time spent in their respective geographical areas, enabling precise attribution of UE behavior to specific events rather than losing information about surrounding events
Solution Approach 2:
The patent changes the analysis parameters by incorporating velocity-weighted dwelling values that combine both the duration UE spends near a POI and the velocity at which UE moves. This parameter transformation allows the system to distinguish between UE stopping to interact with a POI versus merely passing by, thereby improving event attribution accuracy while accounting for multiple simultaneous events
2Productivity
If conventional systems attribute traffic changes to POI events, then simple event impact measurement is achieved, but false attribution occurs when multiple nearby events affect UE paths
Solution Approach 1:
The patent applies dynamics by making the attribution process adaptive through velocity-weighted calculations. The system dynamically adjusts the dwelling value for each POI based on real-time UE velocity and path information, allowing it to reliably distinguish which event actually influenced UE behavior even when multiple events occur simultaneously, thus maintaining both efficiency and reliability
3Device complexity
If UE location data is analyzed without considering velocity and dwell time, then simple path tracking is achieved, but interaction probability with POIs cannot be determined
Solution Approach 1:
The patent applies preliminary action by pre-calculating velocity-weighted dwelling values for each POI along the UE path before determining interaction probability. This preliminary calculation of time spent and velocity at each POI location enables the subsequent accurate determination of interaction probability without requiring complex real-time analysis, thus achieving precise measurement while managing complexity
Data Source
AI summary
Velocity-weighted analysis of UE location data is disclosed. UE velocity can be determined from the change in position and time. UE dwell can also be determined from the change in position and time. UE dwell and UE velocity can be inversely related. UE dwell an UE velocity can be correlated to a likelihood that an event occurrence related to a point of interest affects UE travel between a first and second location. A location of the point of interest can be determined to be in a region corresponding to a path between the first and second location. The region can be associated with the UE dwell and/or UE velocity, such that a probability of interaction can be determined for the event occurrence for the point of interest. The region can comprise a probable UE path based on historical UE data.