Location Profile Segmentation for Wireless Tracking Accuracy
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Solution Overview
Problem
Existing location tracking methods for wireless communication terminals struggle to accurately model and utilize periodic location patterns of users, leading to inefficiencies in location-based services and surveillance applications.
Innovation Solution
A method and system that accumulate location data points over a specified time period to compute characteristic location profiles, correlate these profiles with others, and invoke actions based on identified similarities, improving location accuracy and detecting deviations or connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location data is accumulated over extended periods to improve accuracy, then location tracking precision improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the accumulated location data into discrete time periods (e.g., daily, weekly, monthly cycles) and creates separate location profiles for each period. This segmentation allows the system to manage complex data sets in organized, manageable units, reducing processing complexity while maintaining high accuracy through periodic analysis of location patterns.
2Reliability
If periodic location patterns are modeled to detect user behaviors, then location-based service quality improves, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary computation by pre-calculating location profiles and identifying periodic patterns (daily, weekly, monthly cycles) in advance. By preparing these profiles beforehand and storing them for reference, the system reduces real-time processing requirements when detecting user behaviors or providing location-based services, thus decreasing processing time while maintaining high reliability.
3Loss of information
If multiple location profiles are correlated to identify similarities, then detection of connections and patterns improves, but data processing complexity increases
Solution Approach 1:
The patent transforms location data into standardized profile parameters representing periodic patterns (such as typical locations at specific times, travel routes, and activity cycles). By changing the data representation into these standardized parameters, the system can efficiently correlate multiple profiles to identify similarities and connections without overwhelming processing complexity, as the comparison operates on structured parameters rather than raw data sets.
Data Source
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AI summary
Methods and systems for profiling the locations of users of wireless communication terminals. A profiling engine may accept data points regarding a given target. Each data point may indicate a location of the target at a certain measurement time. The profiling engine may accumulate the data points relative to a periodic time scale having a pre-specified time period. The accumulation process produces a location profile of the target. The location profile may convey information regarding the tracked targets. For example, the profiling engine may use the location profile to improve the location accuracy of a target terminal that is idle for long period of time, to detect deviations from the characteristic location pattern of a target, to detect that a certain sensitive location is being visited regularly by a given target, and/or to identify connections among different targets by detecting similarities or correlations between their location profiles.