IP Address Geolocation Prediction Using Trajectory Learning
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Solution Overview
Problem
Existing technologies face inefficiencies in predicting position information corresponding to an IP address, particularly when complex processing is required for collecting and analyzing access logs to associate IP addresses with known positions.
Innovation Solution
A system utilizing a learning model for machine learning that predicts position information by generating a trajectory sequence of user device movements, including IP addresses and position information, and uses this model to efficiently determine the correspondence between IP addresses and geographical locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex processing is used for collecting and analyzing access logs to associate IP addresses with known positions, then position information prediction accuracy is improved, but processing complexity increases
Solution Approach 1:
The system pre-generates trajectory sequences from access logs and trains the machine learning model in advance. This preliminary processing transforms raw access log data into structured trajectory sequences containing IP addresses, position information, and temporal relationships, which are then used to train the model before actual prediction tasks, thereby simplifying real-time processing while maintaining high accuracy
Solution Approach 2:
The patent introduces trajectory sequences as an intermediary data structure between raw access logs and prediction results. These sequences organize IP address, position, and timestamp information in a standardized format that the machine learning model can efficiently process, acting as a bridge that simplifies the overall processing pipeline
2Reliability
If access logs with known position information are collected and processed to predict position information for unknown IP addresses, then position prediction capability is achieved, but processing time increases
Solution Approach 1:
The system performs model training and trajectory sequence generation in advance before actual prediction is needed. By pre-processing the access logs and training the machine learning model beforehand, the system converts a time-consuming online processing task into a fast online prediction task, significantly reducing processing time while maintaining prediction capability
Solution Approach 2:
The patent replaces traditional rule-based or deterministic methods for associating IP addresses with positions with a machine learning-based predictive system. This substitution allows the system to learn complex patterns from trajectory sequences and make accurate predictions without following rigid processing workflows, improving both speed and adaptability
Data Source
AI summary
An information processing apparatus generates a trajectory sequence of a user device that is moving, the trajectory sequence including at least one IP address of the user device and at least one piece of position information representing a region including a position of the user device, acquires an IP address of a target user device as a target IP address; and predicts position information corresponding to the target IP address by inputting the target IP address to a learning model for machine learning that has learned a correspondence relationship between IP addresses and position information using the trajectory sequence.