Browsing Session Identification via Temporal Transaction Patterns
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Solution Overview
Problem
Existing communication systems face challenges in accurately identifying browsing sessions without explicit user interaction, leading to inefficiencies in bandwidth allocation and user experience.
Innovation Solution
A method and apparatus that intercept and classify request-response transactions between a server and client using temporal patterns, identifying start and end times of browsing sessions, and assigning higher delivery priority to transactions within these sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If transaction classification is performed without explicit user interaction, then automation is improved, but measurement precision deteriorates
Solution Approach 1:
The system uses timeout counts as feedback mechanisms to monitor transaction patterns over time. When no transactions occur within a timeout period, the system infers session termination. This feedback loop enables automated detection of browsing session boundaries without user interaction while maintaining reasonable accuracy through temporal pattern analysis.
Solution Approach 2:
The classification system performs self-service by automatically analyzing temporal patterns of transactions to identify browsing sessions without requiring explicit user input or interaction. The system uses inherent transaction timing data to autonomously determine session start and end times, achieving automation while relying on objectively measurable temporal characteristics.
2Productivity
If temporal pattern analysis is used to identify browsing sessions, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the continuous stream of transactions into discrete browsing sessions by identifying start and end times based on temporal patterns. Each session is treated as a separate unit for classification and bandwidth allocation purposes. This segmentation simplifies the analysis by breaking down complex continuous traffic into manageable discrete segments that can be independently evaluated.
Solution Approach 2:
The system performs preliminary classification of transactions as browsing-related or non-browsing based on temporal patterns before actual bandwidth allocation decisions are made. By pre-identifying session boundaries and characteristics, the system prepares classification results in advance, enabling efficient subsequent bandwidth management without adding complexity to the real-time allocation process.
Data Source
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AI summary
A method includes intercepting multiple request-response transactions communicated between a server (38) and a client (32) operated by a user. An identification of which of the transactions were generated in a browsing session conducted by the user, and which of the transactions do not belong to any browsing session, is made by evaluating a criterion that depends on a temporal pattern of the transactions.