Geo-Temporal Analysis for Suspect Relationship Inference
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Solution Overview
Problem
Traditional methods for investigating suspects through telephone calling records are limited, as individuals can evade detection by not directly communicating, leading to high false positive rates and missed true suspects.
Innovation Solution
A geo-temporal analysis system that analyzes telecommunications-event records, including non-call-related events and fine-resolution location data, to infer relationships between individuals based on their geographic and temporal patterns, even when there is no direct communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional telephone calling records are used for suspect identification, then direct communication between suspects can be detected, but suspects can evade detection by not directly communicating, leading to high false positive rates
Solution Approach 1:
The patent introduces geo-temporal data as an intermediary to detect relationships between suspects. Instead of relying solely on direct telecommunications, the system uses location and time information from network records to infer indirect relationships. The geo-temporal analysis system acts as a mediator that processes location data, call records, and other network information to identify patterns that indicate collaboration between suspects, even when direct communication is not recorded.
Solution Approach 2:
The patent adds a new dimension of analysis by incorporating geographic and temporal data alongside traditional call records. This multi-dimensional approach transforms the investigation from a single-channel (telecommunications only) to a multi-channel system that analyzes location, time, and communication patterns simultaneously. The system processes data from multiple sources including location information, call detail records, and network event logs to build a comprehensive profile of suspect relationships.
2Measurement precision
If fine-resolution location data is collected from telecommunications events, then geo-temporal patterns can be precisely tracked, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct functional modules: data collection module, geo-temporal pattern analysis module, relationship inference module, and visualization module. Each module processes specific types of data and performs specific analyses, making the overall system more manageable. The system divides location data processing from communication pattern analysis, allowing each component to be optimized independently while working together to identify suspect relationships.
Solution Approach 2:
The system incorporates feedback mechanisms where the geo-temporal analysis continuously refines its patterns based on new data. The relationship inference engine receives geo-temporal patterns and communication records, processes them to identify relationships, and feeds back updated patterns for further analysis. This iterative feedback process allows the system to adapt to changing data patterns and improve its accuracy over time while managing processing complexity through systematic feedback loops.
Data Source
AI summary
An illustrative geo-temporal analysis system analyzes telecommunications-event records and other records associated with wireless terminals to infer a collaborative relationship between users who do not telecommunicate with each other, based on how precisely a first geo-temporal pattern matches a second geo-temporal pattern. When a collaborative relationship is inferred, the system transmits an indication thereof and a request for an estimated location of the respective wireless terminals.