IP Address Classification via Geolocation and Usage Patterns
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Solution Overview
Problem
Current geolocation tools cannot effectively differentiate between various locations used by a user, such as home, work, and travel, leading to unclear context for location-based information derived from IP addresses, which hinders personalized service provisioning and security measures.
Innovation Solution
A system that classifies IP addresses into location-based categories by identifying home, travel, and work addresses through geolocation data analysis, using a distance parameter and filtering out proxy and VPN addresses to derive mobility patterns and enhance security, advertising, and network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current geolocation tools are used to map IP addresses to city-level positions, then location information can be obtained, but the context of these positions remains unknown and cannot differentiate between home, work, and travel locations
Solution Approach 1:
The patent segments location information into distinct categories (home, work, travel) by analyzing user behavior patterns across multiple IP addresses. Instead of treating all locations uniformly, the system divides them into meaningful segments based on usage frequency, temporal patterns, and geographic clustering, thereby providing contextual precision without requiring complex manual classification
Solution Approach 2:
The system enables locations to self-categorize by automatically analyzing user interaction patterns with IP addresses. The classification emerges organically from observed behavior data rather than requiring external manual labeling, allowing the system to derive context autonomously from usage patterns while maintaining scalability
2Reliability
If all IP addresses are classified without filtering, then comprehensive location data is captured, but inconsistencies arise from proxies and VPN addresses
Solution Approach 1:
The patent extracts and removes unreliable IP address data (proxies and VPNs) from the classification process by identifying characteristic patterns associated with these services. By taking out these problematic elements, the system maintains classification consistency without needing to process every single IP address in detail, thereby balancing reliability with processing efficiency
Solution Approach 2:
The system performs preliminary filtering of IP addresses before full classification by identifying and excluding known proxy and VPN ranges upfront. This preliminary action prevents inconsistent data from entering the main classification pipeline, ensuring reliability while reducing the overall processing burden by eliminating problematic data points early
3Adaptability or versatility
If location-based services are provided without contextual differentiation, then service provisioning is simplified, but personalized services cannot be effectively delivered
Solution Approach 1:
The patent implements dynamic service adaptation by continuously monitoring user location patterns and adjusting service provisioning based on the current context (home, work, or travel). The system dynamically categorizes locations and adjusts service behavior accordingly, enabling personalization without requiring static, pre-configured rules for each scenario
Solution Approach 2:
The classification system serves multiple functions simultaneously: it enables personalized service provisioning, enhances security measures, optimizes network management, and provides analytics insights. By creating a universal location context framework, the system supports diverse applications without requiring separate implementation for each use case, thereby managing complexity through multi-functionality
Data Source
AI summary
A system to automatically classify types of IP addresses associated with a user. Information, such as user names, machine information, IP address, etc., may be obtained from logs. For each user or host in the logs, home IP addresses are identified from IP addresses where the user or host shows a predetermined level of activity. Travel IP addresses are identified, which are IP addresses at locations greater than a predetermined distance from the home IP addresses, as determined from geolocation data. A pattern analysis may be performed to determine which of the home IP addresses are work IP addresses associated with the user or host. The system may thus provide a classification of a user's or host's associated IP addresses as being one of travel, home, and work IP addresses. From this classification, mobility patterns may be derived, as well as applications to enhance security, advertising, search and network management.