Dynamic User Identification via Context Segmentation
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Solution Overview
Problem
Online service providers face challenges in efficiently providing information about other online users accessing the same or similar online locations due to the vast number of users and wealth of available information, which hinders real-time user identification and interaction.
Innovation Solution
A dynamic user identification system that includes a context determination service, identification service, information storage service, and informing service, which determines the online context of a user and identifies other users based on traits, allowing for real-time dynamic user identification and interaction through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the online service provider attempts to provide information about all users accessing online locations, then the quantity of user information available increases, but the system complexity and computational burden increase significantly
Solution Approach 1:
The patent segments the vast user base into smaller, manageable groups based on shared contexts (online locations, traits, interests). Instead of processing all users simultaneously, the system divides them into context-specific subsets, making the identification process computationally feasible while maintaining comprehensive coverage across multiple contexts.
Solution Approach 2:
The system applies local quality by providing different levels of user information detail based on the specific context and user needs. Rather than uniformly processing all user data with equal detail, the system adapts the depth and scope of information provided to match the relevance and importance of each contextual scenario.
2Loss of information
If the system processes and stores wealth of information about all users, then the completeness of user information increases, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing user information as users join online contexts. User profiles, traits, and contextual associations are established in advance, allowing the system to quickly retrieve and match users without performing intensive computations at the moment of identification queries.
Solution Approach 2:
The system implements partial action by focusing processing efforts on the most relevant user attributes and contexts for each specific identification task. Rather than processing all possible user information equally, the system selectively processes only the necessary subset of data required for the current contextual matching operation.
3Ease of operation
If the system identifies and presents information about multiple users in real-time, then the user interaction capability improves, but the computational load and system response time worsen
Solution Approach 1:
The system applies dynamics by continuously updating user contexts and associations as users move between online locations and as new users join or leave contexts. The identification system dynamically adapts to changing conditions in real-time, maintaining accurate user-groupings without requiring complete re-processing of all user data with each change.
Solution Approach 2:
The system implements feedback mechanisms where user interactions, context changes, and identification results are continuously monitored and used to refine future identification operations. This feedback loop allows the system to learn from past operations and optimize its processing strategies, improving response times while maintaining interaction capability.
Data Source
AI summary
Informing a user of a large scale network dynamically of other network users includes determining dynamically an online context of the user. Other users presently within the online context of the user are identified and trait information is stored that is related essentially only to the user or to the other users in a users store associated with the online context. The user is informed dynamically of the other users based on the stored trait information, such as, for example, an age or other demographic identifier, or information indicative of an expertise, interest, preference, user type and/or other quality of the user or of the other individual.