User Activity Data Analyzer for Shared Terminal Profiling
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Solution Overview
Problem
Current systems fail to accurately differentiate between users sharing a common computer or terminal, leading to inaccurate demographic analysis and ineffective targeted advertising, as they assume a single user's activity and lack the ability to recognize multiple users without registration information.
Innovation Solution
A system and method that analyze user activity data to extract profile information, compare it to existing profiles, and generate new profiles for each user, allowing for the identification of different users and tailored advertising without requiring registration, by using a user activity data analyzer, identifier, and profile generator to differentiate users based on site addresses, URLs, click events, and metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the system assumes a single user's activity for simplicity, then the system complexity is reduced, but the user profiling accuracy deteriorates
Solution Approach 1:
The patent segments user activity data into distinct profiles by analyzing patterns such as time of day, duration of sessions, and content preferences. This segmentation allows the system to differentiate between multiple users sharing a terminal without requiring complex authentication mechanisms, thereby improving profiling accuracy while maintaining reasonable system complexity.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes terminal activity data to infer user identities. This intermediary layer uses pattern recognition and statistical analysis to distinguish between different users based on their behavioral patterns, enabling accurate user profiling without directly observing or requiring input from the users themselves.
2Measurement precision
If the system requires registration information to identify users, then the user identification accuracy is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent implements self-service user identification by having the system automatically detect and profile users based on their natural interaction patterns with the terminal. Users are identified through their behavioral fingerprints—such as typical login times, preferred content categories, and session durations—without needing to manually register or provide identifying information. This maintains high identification accuracy while significantly improving ease of operation.
Solution Approach 2:
The patent replaces the mechanical registration system with an automated pattern recognition mechanism. Instead of relying on explicit user registration data, the system uses software-based analysis of behavioral patterns to infer user identities. This substitution eliminates the need for users to manually provide identification information while maintaining or improving identification accuracy through sophisticated data analysis.
3Loss of information
If the system uses cookies and IP addresses to track users, then the user tracking capability is improved, but the loss of information increases due to anonymity and IP changes
Solution Approach 1:
The patent adds a new dimension to user tracking by analyzing temporal and behavioral patterns rather than relying solely on static identifiers like cookies and IP addresses. The system examines time-of-day patterns, session durations, and content preference sequences to create dynamic user profiles. This dimensional shift from static identification to dynamic behavioral analysis overcomes the limitations of cookie-based tracking and IP address changes, improving both information availability and tracking reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors and adjusts user profiles based on observed behavioral patterns. As users interact with the terminal, the system receives feedback about their actual usage patterns and updates their profiles accordingly. This feedback loop allows the system to adapt to changing user behaviors and overcome the instability of IP address changes or cookie limitations, maintaining reliable tracking even when traditional identifiers become unreliable.
Data Source
AI summary
A system and method profiles different users having a common terminal identifier in communication system. The system comprises a user activity data analyzer for extracting profile data from user terminal activity data, a user identifier for determining whether the profile data corresponds to a profile data history associated with the user terminal, and a user profile generator for generating a profile data history from the profile data for another user to be associated with the terminal in response to the profile data not corresponding to the profile data history associated with the user terminal. The user activity data analyzer extracts site addresses, URLs, click event data, metadata and other user activity from a session log to compile information useful for assessing a user's interests. This extracted profile data may then be compared to a profile history previously generated and associated with the terminal identifier. A low level of correspondence between the extracted profile data and the profile history associated with the terminal identifier indicates that a different user is generating the user activity data. The user profile generator then builds a profile history from the extracted profile data and associates it with the terminal identifier. The profile histories are provided different user identifiers. Upon subsequent detection of the terminal identifier, the profile data extracted from the user activity is compared to both profile histories to determine which user is navigating the site. Once sufficient profile data has been extracted to determine which profile history corresponds to the extracted data, advertising content that corresponds to the identified user may be selected and included in the content requested by the current user.