Event-Based Clustering for Real-Time Collaboration Groups
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Solution Overview
Problem
Legacy approaches for identifying collaboration groups in dynamic shared content environments consume excessive computing, storage, and networking resources, and fail to provide real-time relevant cluster recommendations due to delays in processing user information records.
Innovation Solution
Implement event-based clustering techniques that respond to a stream of entity interaction events to continuously update collaboration clusters and cluster affinity scores, reducing the reliance on historical logs and enabling real-time formation and maintenance of collaboration groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy techniques are used for identifying collaboration groups, then comprehensive analysis of user interactions can be performed, but excessive computing resources, storage resources, and networking resources are consumed
Solution Approach 1:
The patent segments the collaboration identification process into multiple components: event subscription modules that listen for specific interaction types, incremental scoring mechanisms that update only affected users, and modular affinity calculations. This segmentation allows the system to process only relevant data portions rather than performing comprehensive analysis of all user interactions, significantly reducing computing resource consumption while maintaining identification accuracy.
Solution Approach 2:
The system implements dynamic collaboration group identification by continuously updating cluster affinity scores in real-time as new interaction events occur. Instead of periodic batch processing, the system dynamically adjusts scores incrementally, allowing collaboration groups to evolve naturally as user interactions change. This dynamic approach reduces the need for repeated comprehensive analyses while keeping results current and relevant.
2Reliability
If legacy techniques process user information records, then collaboration clusters can be identified, but delays occur in providing real-time relevant cluster recommendations
Solution Approach 1:
The system performs preliminary actions by pre-subscribing to event streams and pre-computing base affinity scores for all users. When interaction events occur, the system has already prepared the framework for quick updates, needing only to incrementally adjust scores rather than performing full recomputations. This preliminary preparation enables real-time response to user interactions without processing delays.
Solution Approach 2:
The patent implements continuous collaboration group identification through persistent event stream subscriptions and incremental score updates. The system maintains continuous readiness to process interactions by keeping event listeners active and scores updated in real-time, eliminating the start-stop nature of batch processing. This continuity ensures that collaboration recommendations are always current without introducing delays between user actions and system responses.
3Loss of information
If historical logs are stored and processed, then comprehensive collaboration patterns can be analyzed, but storage resources and processing overhead increase
Solution Approach 1:
The system extracts only the essential elements needed for collaboration identification: interaction event types, user identifiers, and affinity score components. Instead of storing complete historical logs of all user activities, the system extracts and retains only the specific data elements that contribute to cluster affinity calculations. This extraction approach maintains the ability to analyze collaboration patterns while minimizing storage requirements by discarding redundant historical information.
Solution Approach 2:
The patent transforms historical interaction data into simplified affinity score parameters that capture collaboration patterns without requiring storage of original detailed logs. By converting complex historical records into aggregated score values that reflect collaboration strength, the system maintains pattern analysis capability while dramatically reducing storage requirements. The parameter transformation allows the system to work with compact numerical representations rather than voluminous historical data.
Data Source
AI summary
Systems for forming and maintaining spontaneous networks of collaborators in shared content management systems. A shared content management system supports user interactions with content objects. A service of the content management system monitors occurrences of interactions between users and objects. The users are associated with collaboration groups. To generate recommendations of groups other than the collaboration group or groups in which a particular user is already a member, a method embodiment receives entity relationship scores from the service. An entity relationship score quantifies a relationship between two subject entities that are common to a particular entity interaction event. The method then assigns the subject entities to one or more spontaneously-generated clusters. As clusters are formed and populated, cluster affinity scores are continuously calculated. Periodically, a recommended cluster is selected based on a corresponding cluster affinity score. A recommended cluster is named based on the member entities of the recommended cluster.